CN113208645A - Method and system for providing enhanced ultrasound images - Google Patents
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Abstract
The invention provides a method and system for providing an enhanced ultrasound image. The present invention provides a method and system for enhancing ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power. The method may include acquiring, by an ultrasound system, a first ultrasound image at a first acoustic power. The method may include processing, by at least one processor, the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power. The method may include presenting, at the display system, a second ultrasound image simulating second acoustic power.
Description
Technical Field
Certain embodiments relate to ultrasound imaging. More particularly, certain embodiments relate to methods and systems for providing enhanced ultrasound images simulating acquisition at high acoustic power by processing ultrasound images acquired at low acoustic power.
Background
Ultrasound imaging is a medical imaging technique for imaging organs and soft tissue in the human body. Ultrasound imaging uses real-time, non-invasive high frequency sound waves to produce a series of two-dimensional (2D) images and/or three-dimensional (3D) images.
Currently, the U.S. Food and Drug Administration (FDA) requires diagnostic ultrasound imaging systems to operate under certain acoustic output limits for safety reasons. However, when the transmission acoustic power of the same scan target is reduced, the quality of the ultrasound image deteriorates. Contrast Enhanced Ultrasound (CEUS) involves the use of microbubble contrast agents and specialized imaging techniques to better visualize blood flow and tissue perfusion information. Thus, CEUS transmits much lower acoustic power (e.g., about 10%) than conventional limits to avoid destruction of microbubbles of contrast agents. In CEUS, B-mode images with low transmit power are typically displayed alongside contrast-enhanced images to provide anatomical reference. Due to the low transmit power, the reference image typically suffers from less penetration, less contrast, lower spatial resolution, and higher background noise than B-mode images acquired at typical acoustic power.
Furthermore, the american medical ultrasound society (AIUM) and British Medical Ultrasound Society (BMUS) guidelines advocate reducing the acoustic power of obstetrical ultrasound scans, as well as reducing the regulatory limits of ophthalmic ultrasound scans. These ultrasound images are also of lower quality than images acquired at higher acoustic power.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present disclosure as set forth in the remainder of the present application with reference to the drawings.
Disclosure of Invention
A system and/or method for enhancing ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, substantially as shown in and/or described in connection with at least one of the figures, as set forth more completely in the claims.
These and other advantages, aspects, and novel features of the present disclosure, as well as details of an illustrated embodiment thereof, will be more fully understood from the following description and drawings.
Drawings
Fig. 1 is a block diagram of an exemplary ultrasound system and training system operable to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, in accordance with various embodiments.
Fig. 2 is a flow diagram illustrating exemplary steps that may be used to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, in accordance with various embodiments.
Fig. 3 is a flow diagram illustrating exemplary steps that may be used to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, in accordance with various embodiments.
Detailed Description
Certain embodiments may be found in methods and systems for enhancing ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power. Various embodiments have the technical effect of providing higher quality ultrasound images by enhancing images acquired with low or limited acoustic output without destroying contrast agents or exceeding safety limits. Aspects of the present disclosure have the technical effect of enhancing image quality for ultrasound applications and/or modes requiring lower or limited transmitted acoustic power.
The foregoing summary, as well as the following detailed description of certain embodiments, will be better understood when read in conjunction with the appended drawings. To the extent that the figures illustrate diagrams of the functional blocks of various embodiments, the functional blocks are not necessarily indicative of the division between hardware circuitry. Thus, for example, one or more of the functional blocks (e.g., processors or memories) may be implemented in a single piece of hardware (e.g., a general purpose signal processor or a block of random access memory, hard disk, or the like) or multiple pieces of hardware. Similarly, the programs may be stand alone programs, may be incorporated as subroutines in an operating system, may be functions in an installed software package, and the like. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings. It is to be further understood that the embodiments may be combined, or that other embodiments may be utilized and that structural, logical, and electrical changes may be made without departing from the scope of the various embodiments. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims and their equivalents.
As used herein, an element or step recited in the singular and proceeded with the word "a" or "an" should be understood as not excluding plural said elements or steps, unless such exclusion is explicitly recited. Furthermore, references to "exemplary embodiments," "various embodiments," "certain embodiments," "representative embodiments," etc., are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments "comprising," "including," or "having" an element or a plurality of elements having a particular property may include additional elements not having that property.
In addition, as used herein, the term "image" broadly refers to both a viewable image and data representing a viewable image. However, many embodiments generate (or are configured to generate) at least one viewable image. Further, as used herein, the phrase "image" is used to refer to ultrasound modes, such as B-mode (2D mode), M-mode, three-dimensional (3D) mode, CF mode, PW doppler, CW doppler, Contrast Enhanced Ultrasound (CEUS), and/or sub-modes of B-mode and/or CF, such as harmonic imaging, Shear Wave Elastography (SWEI), strain elastography, TVI, PDI, B-flow, MVI, UGAP, and in some cases MM, CM, TVD, where "image" and/or "plane" includes a single beam or multiple beams.
Further, as used herein, the term processor or processing unit refers to any type of processing unit that can perform the required computations required by the various embodiments, such as single core or multi-core: a CPU, an Accelerated Processing Unit (APU), a Graphics Processing Unit (GPU), a DSP, an FPGA, an ASIC, or a combination thereof.
It should be noted that various embodiments of generating or forming images described herein may include processes for forming images that include beamforming in some embodiments, and do not include beamforming in other embodiments. For example, the image may be formed without beamforming, such as by multiplying a matrix of demodulated data by a matrix of coefficients, such that the product is an image, and wherein the process does not form any "beams. In addition, the formation of an image may be performed using a combination of channels (e.g., synthetic aperture techniques) that may result from more than one transmit event.
In various embodiments, for example, sonication is performed in software, firmware, hardware, or a combination thereof to form an image, including ultrasound beamforming, such as receive beamforming. One specific implementation of an ultrasound system having a software beamformer architecture formed in accordance with various embodiments is shown in figure 1.
Fig. 1 is a block diagram of an exemplary ultrasound system 100 and training system 200 operable to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, in accordance with various embodiments. Referring to fig. 1, an ultrasound system 100 and a training system 200 are shown. Ultrasound system 100 includes a transmitter 102, an ultrasound probe 104, a transmit beamformer 110, a receiver 118, a receive beamformer 120, an A/D converter 122, an RF processor 124, an RF/IQ buffer 126, a user input device 130, a signal processor 132, an image buffer 136, a display system 134, and an archive 138.
The transmitter 102 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to drive the ultrasound probe 104. The ultrasound probe 104 may include a two-dimensional (2D) array of piezoelectric elements. The ultrasound probe 104 may include a set of transmit transducer elements 106 and a set of receive transducer elements 108 that generally constitute the same elements. In certain embodiments, the ultrasound probe 104 is operable to acquire ultrasound image data covering at least a substantial portion of an anatomical structure, such as a heart, a blood vessel, or any suitable anatomical structure.
The transmit beamformer 110 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to control the transmitter 102 to drive the set of transmit transducer elements 106 through the transmit sub-aperture beamformer 114 to transmit ultrasonic transmit signals into a region of interest (e.g., a human, an animal, a subsurface cavity, a physical structure, etc.). The transmitted ultrasound signals may be backscattered from structures in the object of interest, such as blood cells or tissue, to generate echoes. The echoes are received by the receiving transducer elements 108.
A set of receive transducer elements 108 in the ultrasound probe 104 is operable to convert the received echoes to analog signals, sub-aperture beamformed by a receive sub-aperture beamformer 116, and then transmitted to a receiver 118. The receiver 118 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to receive signals from the receive sub-aperture beamformer 116. The analog signal may be transmitted to one of a/D converters 122.
The plurality of a/D converters 122 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to convert analog signals from the receiver 118 to corresponding digital signals. A plurality of a/D converters 122 are disposed between the receiver 118 and the RF processor 124. The present disclosure is not limited in this respect, though. Thus, in some embodiments, multiple a/D converters 122 may be integrated within receiver 118.
The RF processor 124 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to demodulate digital signals output by the plurality of a/D converters 122. According to one embodiment, the RF processor 124 may include a complex demodulator (not shown) that may be used to demodulate the digital signals to form I/Q data pairs representative of corresponding echo signals. The RF or I/Q signal data may then be passed to an RF/IQ buffer 126. The RF/IQ buffer 126 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to provide temporary storage of RF or I/Q signal data generated by the RF processor 124.
The receive beamformer 120 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to perform digital beamforming processing to, for example, sum delayed channel signals received from the RF processor 124 via the RF/IQ buffer 126 and output a beamformed signal. The resulting processed information may be the beam summation signal output from the receive beamformer 120 and passed to the signal processor 132. According to some embodiments, the receiver 118, the plurality of a/D converters 122, the RF processor 124, and the beamformer 120 may be integrated into a single beamformer, which may be digital. In various embodiments, the ultrasound system 100 includes a plurality of receive beamformers 120.
The user input device 130 may be used to enter patient data, scan parameters, settings, select protocols and/or templates, etc. In an exemplary embodiment, the user input device 130 is operable to configure, manage and/or control the operation of one or more components and/or modules in the ultrasound system 100. In this regard, the user input device 130 may be used to configure, manage and/or control the operation of the transmitter 102, ultrasound probe 104, transmit beamformer 110, receiver 118, receive beamformer 120, RF processor 124, RF/IQ buffer 126, user input device 130, signal processor 132, image buffer 136, display system 134 and/or archive 138. User input device 130 may include one or more buttons, one or more rotary encoders, a touch screen, motion tracking, voice recognition, a mouse device, a keyboard, a camera, and/or any other device capable of receiving user instructions. In certain embodiments, for example, one or more of the user input devices 130 may be integrated into other components (such as the display system 134 or the ultrasound probe 104). For example, the user input device 130 may include a touch screen display.
The signal processor 132 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to process the ultrasound scan data (i.e., the summed IQ signals) to generate an ultrasound image for presentation on the display system 134. The signal processor 132 is operable to perform one or more processing operations according to a plurality of selectable ultrasound modalities on the acquired ultrasound scan data. In an exemplary embodiment, the signal processor 132 may be used to perform display processing and/or control processing, and the like. As echo signals are received, acquired ultrasound scan data may be processed in real-time during a scan session. Additionally or alternatively, the ultrasound scan data may be temporarily stored in the RF/IQ buffer 126 during a scan session and processed in a less real-time manner in an online operation or an offline operation. In various implementations, the processed image data may be presented at display system 134 and/or may be stored at archive 138. Archive 138 may be a local archive, Picture Archiving and Communication System (PACS), or any suitable device for storing images and related information.
The signal processor 132 may be one or more central processing units, graphics processing units, microprocessors, microcontrollers, or the like. For example, the signal processor 132 may be an integrated component, or may be distributed in various locations. In an exemplary embodiment, the signal processor 132 may include an analog acoustic power enhancement processor 140 and may be capable of receiving input information from the user input device 130 and/or the profile 138, generating output that may be displayed by the display system 134, and manipulating the output in response to input information from the user input device 130, and the like. The signal processor 132 and the analog acoustic power enhancement processor 140 may be capable of performing, for example, any of the methods and/or sets of instructions discussed herein in accordance with various embodiments.
The ultrasound system 100 is operable to continuously acquire ultrasound scan data at a frame rate appropriate for the imaging situation in question. Typical frame rates are in the range of 20-120, but may be lower or higher. The acquired ultrasound scan data may be displayed on the display system 134 at the same frame rate, or at a slower or faster display rate. An image buffer 136 is included for storing processed frames of acquired ultrasound scan data that are not scheduled for immediate display. Preferably, the image buffer 136 has sufficient capacity to store at least several minutes of frames of ultrasound scan data. The frames of ultrasound scan data are stored in a manner that is easily retrievable therefrom according to their acquisition order or time. The image buffer 136 may be embodied as any known data storage medium.
The signal processor 132 may comprise an analog acoustic power enhancement processor 140 comprising suitable logic, circuitry, interfaces and/or code that may be operable to process the low acoustic power ultrasound image to generate an ultrasound image that simulates high acoustic power. For example, the analog acoustic power enhancement processor 140 may receive ultrasound images acquired at low acoustic power, such as Contrast Enhanced Ultrasound (CEUS) reference images, obstetrical ultrasound images, ophthalmic ultrasound images, images with acoustic power levels within the U.S. Food and Drug Administration (FDA) limits, and/or any suitable ultrasound images acquired at low acoustic power. The analog acoustic power enhancement processor 140 may be configured to generate ultrasound images that enhance contrast resolution, spatial resolution, reduce noise, and the like, such that the generated ultrasound images appear to be acquired at higher acoustic power. In representative embodiments, the simulated acoustic power enhancement processor 140 may include an artificial intelligence model/deep neural network (e.g., a convolutional neural network) and/or may utilize any suitable form of artificial intelligence image processing techniques or machine learning processing functions configured to process ultrasound images acquired at low acoustic power to generate ultrasound images with simulated higher acoustic power.
The analog acoustic power enhancement processor 140 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power. In various embodiments, the simulated acoustic power enhancement processor 140 may deploy a deep neural network (e.g., an artificial intelligence model) that may be composed of, for example, an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. Each layer may be made up of a plurality of processing nodes, which may be referred to as neurons. For example, the simulated acoustic power enhancement processor 140 may infer an artificial intelligence model that includes an input layer with neurons for each pixel or group of pixels from a scan plane of the anatomical structure. The output layer may have neurons corresponding to enhanced pixels that simulate the acquisition at higher acoustic power of the generated ultrasound image. For example, the output layer may generate an enhanced ultrasound image with reduced noise and increased contrast resolution and spatial resolution to simulate a greater acoustic power than that of the acquisition of the input ultrasound image. Each neuron of each layer may perform a processing function and pass the processed ultrasound image information to one neuron of a plurality of neurons of a downstream layer for further processing. For example, neurons of the first layer may learn to identify structural edges in the ultrasound image data. Neurons of the second layer may learn to recognize shapes based on detected edges from the first layer. Neurons of the third layer may learn the location of the identified shape relative to landmarks in the ultrasound image data. The processing of the inferred deep neural network (e.g., convolutional neural network) performed by the analog acoustic power enhancement processor 140 may enhance the low acoustic power ultrasound image data with a high degree of probability.
In an exemplary embodiment, the analog acoustic power enhancement processor 140 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to process the low acoustic power ultrasound image by registering and matching the low acoustic power ultrasound image with a previously acquired ultrasound image of the same patient anatomy acquired at a higher acoustic power. The simulated acoustic power enhancement processor 140 may be configured to blend and/or combine the registered and matched low acoustic power images and high acoustic power images to generate an enhanced ultrasound image simulating an acoustic power higher than that of the low acoustic power ultrasound image.
The analog acoustic power enhancement processor 140 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to register and match the low acoustic power ultrasound images with the high acoustic power ultrasound images. In various embodiments, the analog acoustic power enhancement processor 140 may deploy a deep neural network that may be composed of, for example, an input layer, an output layer, and one or more hidden layers between the input and output layers. Each layer may be made up of a plurality of processing nodes, which may be referred to as neurons. For example, the simulated acoustic power enhancement processor 140 may infer an artificial intelligence model that includes an input layer with neurons for each pixel or group of pixels from a scan plane of the anatomical structure. The output layer may have neurons corresponding to the locations of the landmarks and/or identified anatomical structures. For example, the output layer may include neurons for neural locations, vascular locations, bone locations, organ locations, or any suitable anatomical locations. Each neuron of each layer may perform a processing function and pass the processed ultrasound image information to one neuron of a plurality of neurons of a downstream layer for further processing. For example, neurons of the first layer may learn to identify structural edges in the ultrasound image data. Neurons of the second layer may learn to recognize shapes based on detected edges from the first layer. Neurons of the third layer may learn the location of the identified shape relative to landmarks in the ultrasound image data. The process of deploying a deep neural network (e.g., a convolutional neural network) performed by the analog acoustic power enhancement processor 140 can register and match anatomical structures in the low acoustic power ultrasound images and the high acoustic power ultrasound images with a high degree of probability.
Still referring to FIG. 1, the display system 134 may be any device capable of communicating visual information to a user. For example, the display system 134 may include a liquid crystal display, a light emitting diode display, and/or any suitable display or displays. The display system 134 may be operable to present ultrasound images and/or any suitable information. For example, the display system 134 may be operable to simulate an enhanced ultrasound image acquired at high acoustic power, or any suitable information, in the presentation.
The archive 138 may be one or more computer-readable memories integrated with the ultrasound system 100 and/or communicatively coupled (e.g., over a network) to the ultrasound system 100, such as a Picture Archiving and Communication System (PACS), a server, a hard disk, a floppy disk, a CD-ROM, a DVD, a compact storage device, a flash memory, a random access memory, a read-only memory, an electrically erasable and programmable read-only memory, and/or any suitable memory. The archive 138 may include, for example, a database, library, information set, or other memory accessed by the signal processor 132 and/or incorporated into the signal processor 132. For example, the archive 138 can store data temporarily or permanently. The archive 138 may be capable of storing medical image data, data generated by the signal processor 132, and/or instructions readable by the signal processor 132, among others. In various embodiments, the archive 138 stores, for example, ultrasound images acquired at low acoustic power, ultrasound images acquired at high acoustic power, enhanced ultrasound images simulating high acoustic power, an artificial intelligence model deployable to generate the enhanced ultrasound images, an artificial intelligence model for registering and matching the high acoustic power ultrasound images and the low acoustic power ultrasound images, and instructions for blending and/or combining the registered and matched high acoustic power ultrasound images and low acoustic power ultrasound images.
The components of the ultrasound system 100 may be implemented in software, hardware, firmware, etc. The various components of the ultrasound system 100 may be communicatively connected. The components of the ultrasound system 100 may be implemented separately and/or integrated in various forms. For example, the display system 134 and the user input device 130 may be integrated as a touch screen display.
Still referring to fig. 1, training system 200 may include a training engine 210 and a training database 220. The training engine 160 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to train neurons of a deep neural network (e.g., an artificial intelligence model) that are inferred (i.e., deployed) by the simulated acoustic power enhancement processor 140. For example, an artificial intelligence model inferred by the simulated acoustic power enhancement processor 140 may be trained to automatically process low acoustic power ultrasound images to generate ultrasound images that simulate acquisition at higher acoustic power by: the contrast resolution, spatial resolution, and noise of received ultrasound images acquired at low acoustic power are enhanced. For example, the training engine 210 may train a deep neural network deployed by the simulated acoustic power enhancement processor 140 using a database 220 of classified paired ultrasound images of various structures. The pair of ultrasound images may include a first ultrasound image acquired at a low acoustic power and a second ultrasound image of the same anatomy acquired at a high acoustic power. In various embodiments, a tissue phantom may be used to simulate the anatomy of a high acoustic power ultrasound image such that FDA limits may be exceeded during an ultrasound scan. For example, the artificial intelligence model inferred by the simulated acoustic power enhancement processor 140 may be trained by the training engine 210 with paired ultrasound images having different acoustic power acquisition levels to train the model deployed by the simulated acoustic power enhancement processor 140 with respect to characteristics of image data acquired at the different acoustic power levels, such as appearance of structural edges, appearance of edge-based structural shapes, appearance and/or presence of noise, and so forth.
As another example, a deep neural network inferred by the analog acoustic power enhancement processor 140 may be trained to automatically register and match ultrasound images acquired at low acoustic power with ultrasound images acquired at high acoustic power. For example, the training engine 210 may train a deep neural network inferred by the analog acoustic power enhancement processor 140 using a database 220 of classified ultrasound images of various structures. The ultrasound images may include images of the anatomy acquired at different acoustic power levels. For example, the deep neural network inferred by the simulated acoustic power enhancement processor 140 may be trained by the training engine 210 with ultrasound images of various anatomical structures at different acoustic power acquisition levels to train the deep neural network deployed by the simulated acoustic power enhancement processor 140 with respect to characteristics of the anatomical structures depicted in the acquired image data (such as appearance of structural edges, appearance of edge-based structural shapes, location of shapes with respect to landmarks in the ultrasound image data, and so forth). In exemplary embodiments, the structural location may include a neural location, a vascular location, a bone location, an organ location, or any suitable anatomical location. The structural information may include information about the edges, shape, and location of organs, nerves, blood vessels, tissues, and the like.
In various embodiments, the database of training images 220 may be a Picture Archiving and Communication System (PACS) or any suitable data storage medium. In certain embodiments, the training engine 210 and/or the training image database 220 may be a remote system communicatively coupled to the ultrasound system 100 via a wired or wireless connection, as shown in fig. 1. Additionally and/or alternatively, components or all of the training system 200 may be integrated with the ultrasound system 100 in various forms.
Fig. 2 is a flow diagram 300 illustrating exemplary steps 302 and 306 that may be used to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, in accordance with various embodiments. Referring to fig. 2, a flowchart 300 is shown that includes exemplary steps 302-306. Certain embodiments may omit one or more steps, and/or perform steps in a different order than the order listed, and/or combine certain steps discussed below. For example, some steps may not be performed in certain embodiments. As another example, certain steps may be performed in a different temporal order than listed below, including concurrently.
At step 302, the ultrasound system 100 acquires ultrasound images at low acoustic power. For example, the ultrasound system 100 may acquire ultrasound images with the ultrasound probe 104 positioned at a scanning location over the region of interest. The acoustic power applied to acquire the images may be low level, such as within FDA limits and/or according to AIUM and/or BMUS guidelines. The acquired ultrasound image may be a CEUS image, an obstetrical ultrasound image, an ophthalmic ultrasound image, or any suitable image.
At step 304, the signal processor 132 of the ultrasound system 100 may process the low acoustic power ultrasound image by inferring an artificial intelligence model to generate a second ultrasound image that simulates high acoustic power. For example, the analog acoustic power enhancement processor 140 of the signal processor 132 may be configured to enhance the contrast resolution, spatial resolution, reduce noise thereof, etc. of the ultrasound image acquired at step 302 to generate an ultrasound image having the appearance of an image acquired at an acoustic power higher than the acoustic power at which the acquired ultrasound image was obtained. The simulated acoustic power enhancement processor 140 may deploy artificial intelligence models, artificial intelligence image processing algorithms, and/or may utilize any suitable form of artificial intelligence image processing techniques or machine learning processing functions configured to process low acoustic power ultrasound images to generate enhanced ultrasound images simulating acquisition at higher acoustic power. For example, the ultrasound image acquired at step 302 may be acquired at an acoustic power below the FDA limit, and the ultrasound image generated at step 304 may simulate an acoustic power exceeding the FDA limit. As another example, the ultrasound image acquired at step 302 may be acquired with a microbubble contrast agent, and the ultrasound image generated at step 304 may be a simulated acoustic power that will disrupt microbubbles of the contrast agent.
At step 306, a second ultrasound image simulating high acoustic power may be presented at display system 134. For example, the analog acoustic power enhancement processor 140 of the signal processor 132 may present the enhanced ultrasound image generated at step 304 that simulates the acquisition at a higher acoustic power level at the display system 134.
Fig. 3 is a flow diagram 400 illustrating exemplary steps 402 and 410 that may be used to enhance ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power, in accordance with various embodiments. Referring to fig. 3, a flow chart 400 is shown that includes exemplary steps 402 through 410. Certain embodiments may omit one or more steps, and/or perform steps in a different order than the order listed, and/or combine certain steps discussed below. For example, some steps may not be performed in certain embodiments. As another example, certain steps may be performed in a different temporal order than listed below, including concurrently.
At step 402, the ultrasound system 100 acquires ultrasound images at high acoustic power. For example, the ultrasound system 100 may acquire ultrasound images with the ultrasound probe 104 positioned at a scanning location over the region of interest. The acoustic power applied to acquire the images may be at a high level, such as at or near the highest level allowed by FDA limits and/or prior to the introduction of microbubble contrast agents in CEUS examinations.
At step 404, the ultrasound system 100 acquires ultrasound images at low acoustic power. For example, the ultrasound system 100 may acquire ultrasound images with the ultrasound probe 104 positioned at a scanning location over the region of interest. The acoustic power applied to acquire the image may be lower than the acoustic power applied at step 402. For example, the acquired ultrasound image may be a CEUS image (i.e., after the microbubble contrast agent has been introduced) or any suitable image. In various embodiments, the image acquired at step 404 has the same patient and the same anatomy as the image acquired at step 402.
At step 406, the signal processor 132 of the ultrasound system 100 may register and match the low acoustic power ultrasound image and the high acoustic power ultrasound image. For example, the analog acoustic power enhancement processor 140 of the signal processor 132 may be configured to register and match the low acoustic power ultrasound image acquired at step 404 with the high acoustic power ultrasound image acquired at step 402. The analog acoustic power enhancement processor 140 may include artificial intelligence image processing algorithms, one or more deep neural networks (e.g., convolutional neural networks), and/or may utilize any suitable form of artificial intelligence image analysis techniques or machine learning processing functions configured to analyze, register, and match the low acoustic power ultrasound images and the high acoustic power ultrasound images.
At step 408, the signal processor 132 of the ultrasound system 100 may combine the low acoustic power ultrasound image and the high acoustic power ultrasound image to generate an ultrasound image simulating an acoustic power greater than the low acoustic power. For example, the analog acoustic power enhancement processor 140 of the signal processor 132 may be configured to combine and/or blend the low acoustic power ultrasound image and the high acoustic power ultrasound image registered and matched at step 406 to generate an enhanced ultrasound image that simulates an acoustic power greater than that of the low acoustic power ultrasound image.
At step 410, the generated ultrasound image simulating an acoustic power greater than the low acoustic power may be presented at display system 134. For example, the analog acoustic power enhancement processor 140 of the signal processor 132 may present the enhanced ultrasound image generated at step 408 that simulates the acquisition at a higher acoustic power level at the display system 134.
Aspects of the present disclosure provide methods 300, 400 and system 100 for enhancing ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power. According to various embodiments, the method 300, 400 may include acquiring 302, 404 a first ultrasound image by the ultrasound system 100 at a first acoustic power. The method 300, 400 may include processing 304, 406, 408, by the at least one processor 132, 140, the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power. The method 300, 400 may include rendering 306, 410 a second ultrasound image simulating second acoustic power at the display system 134.
In an exemplary embodiment, the artificial intelligence model may be inferred by the at least one processor 132, 140 to process the first ultrasound image to generate a second ultrasound image simulating the second acoustic power. In representative embodiments, the first ultrasound image may be acquired with a microbubble contrast agent and the simulated second acoustic power may be the power that will disrupt the microbubble contrast agent. In certain embodiments, the first acoustic power may be within Food and Drug Administration (FDA) limits. The second acoustic power may exceed the FDA limit. In various embodiments, the method 300 may include training an artificial intelligence model inferred by the at least one processor 132, 140 based on the pair-wise training images. Each pair of training images in the pair of training images may include a first training image acquired at an acoustic power and a second training image acquired at an acoustic power higher than the acoustic power of the first training image. In an exemplary embodiment, a second training image of each of the pairs of training images may be acquired from the mock tissue phantom. In a representative embodiment, the method 400 may include acquiring 402, by the ultrasound system 100, a third ultrasound image at a third acoustic power greater than the first acoustic power at the same region of interest prior to acquiring the first ultrasound image. The method 400 may include registering and matching 406, by the at least one processor 132, 140, the first ultrasound image with the third ultrasound image. Processing the first ultrasound image may include combining 408, by the at least one processor 132, 140, the first ultrasound image with the second ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power.
Various embodiments provide a system 100 for enhancing ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power. The system 100 may include an ultrasound system 100, at least one processor 132, 140, and a display system 134. The ultrasound system 100 may be configured to acquire a first ultrasound image at a first acoustic power. The at least one processor 132, 140 may be configured to process the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power. The display system 134 may be configured to present a second ultrasound image simulating the second acoustic power.
In a representative embodiment, the at least one processor 132, 140 may be configured to infer an artificial intelligence model to process the first ultrasound image to generate a second ultrasound image simulating the second acoustic power. In an exemplary embodiment, the ultrasound system 100 may be configured to acquire a first ultrasound image with a microbubble contrast agent and the simulated second acoustic power is the power that will break the microbubble contrast agent. In certain embodiments, the first acoustic power may be within Food and Drug Administration (FDA) limits. The second acoustic power may exceed the FDA limit. In various embodiments, the artificial intelligence model inferred by the at least one processor 132, 140 may be trained based on pairs of training images. Each pair of training images in the pair of training images may include a first training image acquired at an acoustic power and a second training image acquired at an acoustic power higher than the acoustic power of the first training image. In representative embodiments, the second training image of each of the pairs of training images may be acquired from the mock tissue phantom. In an exemplary embodiment, the ultrasound system 100 may be configured to acquire a third ultrasound image at a third acoustic power greater than the first acoustic power at the same region of interest prior to acquiring the first ultrasound image. The at least one processor 132, 140 may be configured to register and match the first ultrasound image with the third ultrasound image. The at least one processor 132, 140 may be configured to process the first ultrasound image by: the first ultrasound image is combined with the second ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power.
Certain embodiments provide a non-transitory computer readable medium having stored thereon a computer program having at least one code segment. The at least one code section is executable by a machine for causing the machine to perform steps 300, 400. Steps 300, 400 may include receiving 302, 404 a first ultrasound image acquired at a first acoustic power. Steps 300, 400 may include processing 304, 406, 408 the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power. Steps 300, 400 may include rendering 306, 410 a second ultrasound image simulating second acoustic power at a display system.
In various embodiments, processing 304 the first ultrasound image to generate a second ultrasound image simulating the second acoustic power is performed by inferring an artificial intelligence model. In certain embodiments, the first ultrasound image may be acquired with a microbubble contrast agent and the simulated second acoustic power may be the power that will disrupt the microbubble contrast agent. In representative embodiments, the first acoustic power may be within Food and Drug Administration (FDA) limits. The second acoustic power may exceed the FDA limit. In an exemplary embodiment, steps 300, 400 may include training an artificial intelligence model based on the pair-wise training images. Each pair of training images in the pair of training images may include a first training image acquired at an acoustic power and a second training image acquired at an acoustic power higher than the acoustic power of the first training image. In various embodiments, step 400 may include receiving 402 a third ultrasound image acquired at a third acoustic power greater than the first acoustic power at the same region of interest prior to receiving the first ultrasound image. Step 400 may include registering and matching 406 the first ultrasound image with the third ultrasound image. The processing 406, 408 of the first ultrasound image may include combining 408 the first ultrasound image with the second ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power.
As used herein, the term "circuitry" refers to physical electronic components (i.e., hardware) as well as configurable hardware, any software and/or firmware ("code") executed by and/or otherwise associated with hardware. For example, as used herein, a particular processor and memory may comprise first "circuitry" when executing one or more first codes and may comprise second "circuitry" when executing one or more second codes. As used herein, "and/or" means any one or more of the items in the list joined by "and/or". For example, "x and/or y" represents any element of the three-element set { (x), (y), (x, y) }. As another example, "x, y, and/or z" represents any element of the seven-element set { (x), (y), (z), (x, y), (x, z), (y, z), (x, y, z) }. The term "exemplary", as used herein, means serving as a non-limiting example, instance, or illustration. As used herein, the terms "e.g., (e.g.)" and "e.g., (for example)" bring forth a list of one or more non-limiting examples, instances, or illustrations. As used herein, a circuit is "operable to" and/or "configured to" perform a function whenever the circuit includes the necessary hardware and code (if needed) to perform the function, regardless of whether execution of the function is disabled or not enabled by certain user-configurable settings.
Other embodiments may provide a computer-readable device and/or a non-transitory computer-readable medium, and/or a machine-readable device and/or a non-transitory machine-readable medium having stored thereon machine code and/or a computer program having at least one code section executable by a machine and/or a computer, thereby causing the machine and/or the computer to perform steps as described herein for enhancing ultrasound images acquired at low acoustic power to simulate acquisition at high acoustic power.
Accordingly, the present disclosure may be realized in hardware, software, or a combination of hardware and software. The present disclosure may be realized in a centralized fashion in at least one computer system, or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited.
Various embodiments may also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods. Computer program in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: a) conversion to another language, code or notation; b) replication takes place in different physical forms.
While the disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the scope thereof. Therefore, it is intended that the disclosure not be limited to the particular embodiments disclosed, but that the disclosure will include all embodiments falling within the scope of the appended claims.
Claims (20)
1. A method, the method comprising:
acquiring, by an ultrasound system, a first ultrasound image at a first acoustic power;
processing, by at least one processor, the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power; and
rendering, at a display system, the second ultrasound image simulating the second acoustic power.
2. The method of claim 1, wherein an artificial intelligence model is inferred by the at least one processor to process the first ultrasound image to generate the second ultrasound image simulating the second acoustic power.
3. The method of claim 2, wherein the first ultrasound image is acquired with a microbubble contrast agent and the simulated second acoustic power is the power that will disrupt the microbubble contrast agent.
4. The method of claim 2, wherein the first acoustic power is within Food and Drug Administration (FDA) limits, and wherein the second acoustic power exceeds the FDA limits.
5. The method of claim 2, comprising training the artificial intelligence model inferred by the at least one processor based on pairs of training images, each pair of the pairs of training images comprising a first training image acquired at an acoustic power and a second training image acquired at an acoustic power higher than the acoustic power of the first training image.
6. The method of claim 5, wherein the second training image of each of the pairs of training images is acquired from a mock tissue phantom.
7. The method of claim 1, comprising:
acquiring, by the ultrasound system, a third ultrasound image at a third acoustic power greater than the first acoustic power at the same region of interest prior to acquiring the first ultrasound image, and
registering and matching, by the at least one processor, the first ultrasound image with the third ultrasound image,
wherein the processing the first ultrasound image comprises combining, by the at least one processor, the first ultrasound image with the second ultrasound image to generate the second ultrasound image simulating the second acoustic power greater than the first acoustic power.
8. A system, the system comprising:
an ultrasound system configured to acquire a first ultrasound image at a first acoustic power;
at least one processor configured to process the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power; and
a display system configured to present the second ultrasound image simulating the second acoustic power.
9. The system of claim 8, wherein the at least one processor is configured to infer an artificial intelligence model to process the first ultrasound image to generate the second ultrasound image simulating the second acoustic power.
10. The system of claim 9, wherein the ultrasound system is configured to acquire the first ultrasound image with a microbubble contrast agent, and the simulated second acoustic power is a power that will disrupt the microbubble contrast agent.
11. The system of claim 9, wherein the first acoustic power is within Food and Drug Administration (FDA) limits, and wherein the second acoustic power exceeds the FDA limits.
12. The system of claim 9, wherein the artificial intelligence model inferred by the at least one processor is trained based on pairs of training images, each pair of the pairs of training images comprising a first training image acquired at an acoustic power and a second training image acquired at an acoustic power higher than the acoustic power of the first training image.
13. The system of claim 12, wherein the second training image of each of the pair of training images is acquired from a mock tissue phantom.
14. The system of claim 8, wherein:
the ultrasound system is configured to acquire a third ultrasound image at a third acoustic power greater than the first acoustic power before acquiring the first ultrasound image at the same region of interest,
the at least one processor is configured to register and match the first ultrasound image with the third ultrasound image, and
the at least one processor is configured to process the first ultrasound image by: combining the first ultrasound image with the second ultrasound image to generate the second ultrasound image simulating the second acoustic power greater than the first acoustic power.
15. A non-transitory computer readable medium having stored thereon a computer program having at least one code section executable by a machine to cause the machine to perform steps comprising:
receiving a first ultrasound image acquired at a first acoustic power;
processing the first ultrasound image to generate a second ultrasound image simulating a second acoustic power greater than the first acoustic power; and
rendering, at a display system, the second ultrasound image simulating the second acoustic power.
16. The non-transitory computer-readable medium of claim 15, wherein the processing the first ultrasound image to generate the second ultrasound image simulating the second acoustic power is performed by inferring an artificial intelligence model.
17. The non-transitory computer readable medium of claim 16, wherein the first ultrasound image is acquired with a microbubble contrast agent and the simulated second acoustic power is a power that will disrupt the microbubble contrast agent.
18. The non-transitory computer-readable medium of claim 16, wherein the first acoustic power is within Food and Drug Administration (FDA) limits, and wherein the second acoustic power exceeds the FDA limits.
19. The non-transitory computer-readable medium of claim 16, comprising training the artificial intelligence model based on paired training images, each pair of the paired training images comprising a first training image acquired at an acoustic power and a second training image acquired at an acoustic power higher than the acoustic power of the first training image.
20. The non-transitory computer readable medium of claim 15, comprising:
receiving a third ultrasound image acquired at a third acoustic power greater than the first acoustic power at the same region of interest prior to receiving the first ultrasound image, and
registering and matching the first ultrasound image with the third ultrasound image,
wherein the processing the first ultrasound image comprises combining the first ultrasound image with the second ultrasound image to generate the second ultrasound image simulating the second acoustic power greater than the first acoustic power.
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| CN109620288A (en) * | 2019-01-16 | 2019-04-16 | 王立坤 | A kind of department of general surgery's abdominal ultrasonic diagnostic device and its application method |
| CN110719755A (en) * | 2017-06-08 | 2020-01-21 | 皇家飞利浦有限公司 | Ultrasonic imaging method |
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| IT1306193B1 (en) * | 1999-01-05 | 2001-05-30 | Lorenzo Manzoni | USE OF ULTRASOUNDS FOR THE TREATMENT OF DADECOMPRESSION SYMPTOMATOLOGY. |
| CN104116523B (en) * | 2013-04-25 | 2016-08-03 | 深圳迈瑞生物医疗电子股份有限公司 | A kind of ultrasonic image analyzes system and the method for analysis thereof |
| WO2018127498A1 (en) * | 2017-01-05 | 2018-07-12 | Koninklijke Philips N.V. | Ultrasound imaging system with a neural network for image formation and tissue characterization |
| US20200060652A1 (en) * | 2018-08-23 | 2020-02-27 | The Board Of Trustees Of The Leland Stanford Junior University | Neural networks-assisted contrast ultrasound imaging |
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